The purpose of the F2PY --Fortran to Python interface generator-- project is to provide connection between Python and Fortran languages. F2PY is a Python extension tool for creating Python C/API modules from (handwritten or F2PY generated) signature files (or directly from Fortran sources). The generated extension modules facilitate: Calling Fortran 77/90/95, Fortran 90/95 module, and C functions from Python. Accessing Fortran 77 COMMON blocks and Fortran 90/95 module data (including allocatable arrays) from Python. Calling Python functions from Fortran or C (call-backs). Automatically handling the difference in the data storage order of multi-dimensional Fortran and Numerical Python (i.e. C) arrays. In addition, F2PY can build the generated extension modules to shared libraries with one command. F2PY uses the scipy_distutils module from SciPy that supports number of major Fortran compilers. F2PY generated extension modules depend on NumPy package that provides fast multi-dimensional array language facility to Python.

References in zbMATH (referenced in 30 articles )

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  1. Krishna Naidoo: MiSTree: a Python package for constructing andanalysing Minimum Spanning Trees (2019) arXiv
  2. Matthieu Ancellin; Frédéric Dias: Capytaine: a Python-based linear potential flow solver (2019) not zbMATH
  3. Peets, Tanel; Tamm, Kert: Mathematics of nerve signals (2019)
  4. Salupere, Andrus; Lints, Martin; Ilison, Lauri: Emergence of solitonic structures in hierarchical Korteweg-de Vries systems (2019)
  5. David Topping; Paul Connolly; Jonathan Reid: PyBox: An automated box-model generator for atmospheric chemistry and aerosol simulations (2018) not zbMATH
  6. Garcia, D.; Ghommem, M.; Collier, N.; Varga, B. O. N.; Calo, V. M.: PyFly: a fast, portable aerodynamics simulator (2018)
  7. Römer, Ulrich; Narayanamurthi, Mahesh; Sandu, Adrian: Solving parameter estimation problems with discrete adjoint exponential integrators (2018)
  8. Ahmed Attia, Adrian Sandu: DATeS: A Highly-Extensible Data Assimilation Testing Suite (2017) arXiv
  9. Langtangen, Hans Petter; Linge, Svein: Finite difference computing with PDEs. A modern software approach (2017)
  10. Pierre Fernique, Christophe Pradal: AutoWIG: Automatic Generation of Python Bindings for C++ Libraries (2017) arXiv
  11. Vidal, Jérémie; Cébron, David: Inviscid instabilities in rotating ellipsoids on eccentric Kepler orbits (2017)
  12. Andersson, C., Führer, C., Åkesson, J.: Assimulo: A unified framework for ODE solvers (2015) not zbMATH
  13. Izaac, Josh A.; Wang, Jingbo B.: \textitpyCTQW: a continuous-time quantum walk simulator on distributed memory computers (2015)
  14. Ying, Jinyong; Xie, Dexuan: A new finite element and finite difference hybrid method for computing electrostatics of ionic solvated biomolecule (2015)
  15. Belson, Brandt A.; Tu, Jonathan H.; Rowley, Clarence W.: Algorithm 945: \textttmodred-- a parallelized model reduction library (2014)
  16. Christopher Strickland; Robert Burdett; Kerrie Mengersen; Robert Denham: PySSM: A Python Module for Bayesian Inference of Linear Gaussian State Space Models (2014) not zbMATH
  17. Xie, Dexuan: New solution decomposition and minimization schemes for Poisson-Boltzmann equation in calculation of biomolecular electrostatics (2014)
  18. Salupere, Andrus; Tamm, Kert: On the influence of material properties on the wave propagation in Mindlin-type microstructured solids (2013)
  19. Xie, Dexuan; Jiang, Yi; Scott, L. Ridgway: Efficient algorithms for a nonlocal dielectric model for protein in ionic solvent (2013)
  20. Corrigan, Andrew; Camelli, Fernando; Löhner, Rainald; Mut, Fernando: Semi-automatic porting of a large-scale Fortran CFD code to GPUs (2012)

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